A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-18
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification.
A well-designed feature representation method and classifier can improve classification accuracy.
In this paper, we construct a new two-stream deep architecture for aerial scene classification.
First, we use two pretrained convolutional neural networks (CNNs) as feature extractor to learn deep features from the original aerial image and the processed aerial image through saliency detection, respectively.
Second, two feature fusion strategies are adopted to fuse the two different types of deep convolutional features extracted by the original RGB stream and the saliency stream.
Finally, we use the extreme learning machine (ELM) classifier for final classification with the fused features.
The effectiveness of the proposed architecture is tested on four challenging datasets: UC-Merced dataset with 21 scene categories, WHU-RS dataset with 19 scene categories, AID dataset with 30 scene categories, and NWPU-RESISC45 dataset with 45 challenging scene categories.
The experimental results demonstrate that our architecture gets a significant classification accuracy improvement over all state-of-the-art references.
American Psychological Association (APA)
Yu, Yunlong& Liu, Fuxian. 2018. A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1130846
Modern Language Association (MLA)
Yu, Yunlong& Liu, Fuxian. A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1130846
American Medical Association (AMA)
Yu, Yunlong& Liu, Fuxian. A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1130846
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130846